Solving AE Problems by a Neural Network

This paper summarizes recent work utilizing an adaptive learning system to the characterization of acoustic emission phenomena. The processing system resembles a neural network including an associative memory. Data is input into the system as a vector composed of either AE signals or their spectra and encoded information about the source. The mapping of AE signals from the sensors to the descriptors of the source and vice versa is accomplished by learning in the system. This is performed by presenting experimental signals to the system and adaptively forming a memory whose output is an autoregression projection of the input. Discrepancies between the input and output are applied in a delta learning rule. Experiments are described which utilize a system running on a minicomputer to process signals from a localized, simulated source of discrete acoustic emission events in a block of material and to process the AE signals generated during a metal drilling operation. It is shown that the characteristics of the source can be estimated from the AE signals or vice versa by the auto-associative recall from the correlation memory.